The Paul Neto Blog

Thursday, March 16, 2017

A three year old child can identify a picture of a dog among pictures of food with more accuracy than a computer (want a few more examples, check here).

Fifty years of computer intelligence development and we are essentially nearing the processing level of a three year old, though in the past five years we've achieved more advancement in this area than we have in the past fifty years. This is all due to the rise of activity in machine learning and what is fundamentally changing how we deal with data and think about insights.

We've moved from creating rule based systems to systems that can learn from data. Machine learning, deep learning, SVM, neural networks, all provide new opportunities. Similarly, the abundance of readily available tools such as R and Python libraries to accomplish processing and analysis not possible just a few short years ago.

For most people, it is a difficult concept to get their mind around though a number of recent developments have started to emerge that highlight the power of this approach. Namely, Tesla and it's efforts around autonomous vehicles. The idea of cars driving themselves is something most individuals can grasp and many may be either fearful or impressed with the concept. Not surprisingly, much of the discussion around artificial intelligence (AI) has been around automation or building autonomous systems that can perform tasks that humans do today. The primary difference with this concept from that of automation in the industrial revolution is back to the core difference of these 'machines' being able to learn as they automate processes.

These learning systems typically work by learning from numerous examples, much like the puppy example in the image above. The more examples, the more attempts, the rate of success increases. This is much how the human brain learns, thus the effort is to build systems that mimic how the human brain works. AI systems can learn either by using techniques to discover patterns in data, or through a process called reinforcement learning. Reinforcement learning is based on the concept on how humans learn, through vast examples of trial and error and establishing a reward system when there is success.

One of the core challenges as a result of these techniques is to have a sufficient number or recurring number of features or examples for the machines to process, find patterns and learn from. Furthermore, understanding the breadth of options to explore becomes increasingly important for reinforcement learning.

Humans have an incredible ability to learn quickly, though are relatively inefficient in being able to transfer knowledge from one entity to another. One of the key benefits of a learning system, is that the intelligence can easily be replicated and applied to other system entities. For example, autonomous cars require the processing of millions of miles of driving, though the true benefit is the accumulated learning across many vehicles and not a single vehicle. Similarly, voice recognition systems such as Siri depend on the collective knowledge to improve it's accuracy.

The true success of AI may indeed depend on the ability to foster the availability of data features and examples for processing. I believe this is where the human assisted element plays an important role. As companies and industries start to adopt these technologies, considerable effort is required to navigate the data elements available for processing and building a reliable data pipeline, even before any machine learning can occur.

One of the richest sources of data for processing is from humans themselves. Increasingly it will become critical for systems to be able to monitor and track human behaviors, their trials, their errors and their successes. How we process data, conduct certain tasks, make our own decisions on what to do next, may provide a rich set of data for learning from.

One area of application where this may become quite fruitful is in digital advertising. Most ad technology of the past decade has been predicated on a process of throwing shit at the wall and seeing what sticks. As the industry has started to mature, standards evolving, emergence of new channels, formats and walled gardens, the utilization of learning systems AI is prime for the next wave of development; and within this realm exists experienced and nuanced ad traffickers and media planners which can clumsily navigate multiple DSPs, Facebook campaigns, search campaigns, and interpret objectives, results and success. The increased complexity of the systems involved, scale of data, and response times required to be efficient and successful, the human powered ad technology is endangered, though I would argue that the human assisted AI for ad technology will still be viable and valuable for quite some time.

Still the biggest problem in most industries and applications is "what to do next". This is where machine learning and AI holds it's greatest promise.

Thursday, April 21, 2016

I am fortunate that with my occupation I get to visit many cities and interesting places across the US, Europe and Mexico. I've become a regular user of Uber and spend way too many hours in lineups whether at an airport, taxi stand, or hotel. As such, it is of paramount importance for me to be efficient in my modes of transit.

I can consider myself as an early adopter of Uber from the beginning, and am often annoyed when I land in s city where Uber is not available. Each time I enter an Uber I make a point of speaking to the driver to get their feedback on the service, their level of satisfaction, and how they perceive their role in the transportation industry.

In large, Uber has been perceived by drivers and the general population, or at least my peers and immediate circle, as a positive experience and service. For many drivers it has helped them achieve a work-life balance, or to fill the gap while they are pursing other dreams (think entrepreneurs, actors, musicians), or as a new career.

While Uber and competitors have been growing in popularity, in many places it has not been accepted with open arms, including legal battles, and even violence. The most interesting aspect of the Uber revolution is how it is helping redefine business models and challenging an industry that has not innovated in decades. It is a model that while may be disruptive in the short and mid-term, will experience major iterations of fine-tuning over the next five or ten years, and evolve into something sustainable and common-place.Though I do not believe that Uber will work everywhere, or should it. Let me provide a few examples.The promise, and premise of Uber, in the early days at least, was to optimize use of all the black limos that sit idle for hours per day, and provide customers with a frictionless way of getting around. The most painful part of taking a taxi was hailing a cab, waiting in the rain hoping one would drive by, then fumbling with cash and coins and trying to figure out an appropriate tip. All in the meantime, hoping not to get ripped off and taken on a scenic ride. It is important to note here that the notion of cost wasn't even part of the equation. It is about the transaction and delivery of service.The free marketplace is intended to permit supply and demand, for the adoption of new products and services as the opposing forces to regulate success and failure.The first classic use case for justifying the existence of Uber is in cities like New York, Toronto and Lisbon. In these cities, from my experience at least, is the classic example where it can be difficult to hail a cab, where many cars are extremely dirty and smell, where drivers will ask you where you are going then say no, where many drivers are rude, and there is little confidence that they won't take you for a longer than necessary ride. I will be clear that this is not every driver and not every experience, but it is true in enough incidences that it spoils it for all the good, honest, hard working drivers out there. The working conditions for many of these drivers simply would not be tolerated elsewhere. In some of these locales, the cars and 'badges' are owned by monopolies, and is a very hard industry to make a living.In these cases, the disruption by ride-sharing services is completely justified. There is a product/service that is superior to the existing, it is a better deal for the driver/employer/contractor, and has value for the customer. This should be a happy win-win situation for everyone. In this case, the system has worked.Use case number two. Traveling to unfamiliar places, some of which may be notorious for having unreliable or unsafe taxi services, and many locals stressing to use Uber instead of local taxis. These technology driven services in these cases adds a level of accountability. One can travel with minimal cash, the transaction is paperless, and completely tracked using the technology infrastructure. For the weary traveler, this peace of mind and convenience is well received. Though, as I started out, this system isn't meant to work everywhere. A prime example is London, England. The iconic black cab in London is nothing like that in say a New York or Toronto. While the system is highly regulated, the premise is that anyone who drives a cab and can pick up someone on the street to drive them somewhere, is essentially an ambassador to the city. They carry a lot of responsibility and as such, should be held to very high standards. As a result, as a driver in London, you must take a 3 year study called the "knowledge' which is a grueling study on every inch of London. Need directions, need advice, need a good recommendation of a place to eat, find something out of the way, or the best local spot, there is not a more helpful and knowledgeable resource than the nearest black cab. The black cabs may be pricey, but there is value to what they do and plays a very important role in the operation and culture of the city.Is there value to protect this service, to protect the culture of getting around London. I believe there is. The challenge is in the balance as Uber has threatened this way of life.

These are just a few examples of they benefits and complexity of a changing business model. Though while many of the proponents of the disruptive business model point to the free market capitalist forces as part of the justification, there is a social and moral obligation as well. As much as we all like to think we operate within a free market model, there is a very strong social reality and responsibility. We do not operate in a void of letting the markets take care of everything. We are a charitable society, one that values prosperity, health and general wellness.

My point here is that there are very few occupations where someone with few qualifications, new to the country, or less educated, or in a current bind where they need to make ends meet, can take employment and make it through the week. Some will say Uber does this if fact, and better than the taxi industry, though it does not eliminate the fact that the current taxi industry plays an important role for many today.The bright side is that the ride-sharing, or on-demand economy is still in it's early days and will continue to evolve and iterate. In a few years from now, it'll be very different.

Tuesday, January 13, 2015

Like most of us, I've been a long time fan of Seinfeld. There is no doubt that Seinfeld will go down as one of the best sitcoms of all times. Thanks to Netflix and other streaming servies, like everyone else, I've done my share of binge watching. Of most recent is about six seasons of Californication with David Duchovny.

At first glance, both of these shows are quite different though there is something that many of us find appealing about both. Of course Californication is much edgier, largely due to it's home on Showtime, and Seinfeld being a primetime show during it's run. I've often wondered what Seinfeld would have been like if it was hosted outside of a major network like NBC? Some say Larry David's Curb Your Enthusiasm is what Seinfeld would have been outside of the primetime slot.
While both these shows contrast in some very dramatic ways, I believe what makes both of them so likeable are the ways they are similar. Here are 5 ways Seinfeld and Californication/David Duchovny are strikingly similar.

1. Witty HumorBoth Seinfeld and David Duchovny use witty, quick-fire humor primarily about obvious observations around them in any situation

2. Not so Normal
The characters surrounding Seinfeld and Duchovny are incrementally more dysfunctional that we think they are the normal ones. Each on their own without the context of the supporting characters would be a different story.

3. Guest Stars
Both shows strategically use well placed guest stars. Whether it is Rick Springfield in Californication or Keith Hernandez in Seinfeld, all are well placed and add a special dynamic to both shows.

4. Bi-Coastal Madness
Where Seinfeld is about the neurotic New York, Californication is about the over-the-top LA. Both draw on stereotypes of each place and use established landmarks. Interestingly both venture to the other coast where the characters exhibit new levels of dysfunction which drives them back to their home coastal city.

5. It's Really About Nothing
Not surprisingly, both shows are really about nothing.

Saturday, July 12, 2014

Corner number four at Canadian Tire Motorsports Park, formerly known as Mosport, is the often overlooked and under-appreciated corner. Corner number two, it's double apex, blind corner, odd camber, high speed sweeper gets all the drama and attention. On the contrary, corner number four is probably as exciting if not more important for an aspiring driver.

Most drivers will focus on number two starting cautious and building up momentum and confidence, and lap after lap prepare for it again. Corner number four is as exciting and important for a number of reasons. It is also a great opportunity, particularly for those of us in relatively underpowered cars in

the field. Having an elevation change of about 90 feet (corner number two is about half that), and despite how it looks has camber in your favor which means tons of traction. You can only appreciate this if you have an opportunity to walk the track. What this means is that once positioned correctly you can easily power through the corner gaining tremendous amount of speed in a very short amount of time. It is a great opportunity to make up some distance on those Carrera S's and Turbos.

Secondly, this importance is amplified considering what is coming up next. Turn five A and B. Any way you slice it, turn five is the slowest part of the track. You either try and crank through it or just accept and sacrifice it. As such any progress you can make coming into it (turn four) will give you an advantage. Another benefit is that the approach to corner number five has an incredible steep rising slope. Turn five is so steep that when walking you must climb on your toes. This plays in your favor as you power through corner four and delay braking to the point that it feels too long. The incline of corner five will help you compress and amplify braking power.

As you come out of corner three on the far left side, ensure you approach corner four far to the right. Approaching the crest be sure to turn in early and align so that you are turned in and passing under the Continental sign approximately in the middle. Look far ahead and your goal is to be positioned and aligned tight to the left of track at the bottom of the hill. It may not look like you will get there but hold steady and as soon as you are over the crest, power down and the car will rotate as you come through and align perfectly so you are tight to the left edge at the bottom of the hill. With the elevation change and positive camber you will experience great acceleration and continue accelerating until it feels almost too late. Heel-toe technique is quite important here as you need to shift down quickly as you brake hard. The change in slope will assist in braking and engage as coming in to turn five.

As you build experience through this corner, you will be amazed as you can consistently build time, speed and power. Master this corner and number five becomes is merely a setup for the Mario Andretti Straightaway.

Sunday, June 22, 2014

A couple years ago I took up performance driving as a hobby, so I picked up a little Porsche and joined the Porsche Club of America, Upper Canada Region who put on a great program with their driver education events. I really have no interest in racing but am enticed with learning about the dynamics of speed, grip, and techniques of maneuvering a vehicle around a road track.

or as once known Mosport. Once a regular stop on the F1 circuit, it was neglected for many years though in the past few years Canadian Tire has transformed it into a modern facility. Often regarded as one of the most difficult courses in the world, it is one of the few circuits that has largely been untouched and mostly original. Existing in the oak ridge moraine, it has earned it's reputation due to it's high spend turns, blind apexes and dramatic elevation changes.

Turn 2 is probably it's signature turn and once you drive it, it's like playing a spectacular golf course where you replay certain holes over and over in your mind. Turn two is a double apex left turn sweeper and driving it is best described as a dance with physics, grip and technology. Check out this youtube video of turn 2.

Coming out of pit lane you want to quickly come up to speed as taking turn 2 too slow makes it an awkward turn to maneuver. As you build experience you start to find the groove and speed where things just start to work and you know this when you feel it, no other way to describe it. A quick lap or two around the track to warm up the tires and turn two comes to life.

Coming out of turn one you want to be flat out when you hit the apex and gradually line up to the right to approach turn two, making sure to keep clear of the pit merge lane. Approaching the turn there is a crest which makes it a completely blind corner. The trick to this turn is to stay committed and keep your hands still and keep to the line. As you approach, lightly tap the brakes to shift weight to the front and load traction on your front wheels and while still in the blind you want to make your turn in. The best way to describe the amount of turn in is that if you were going any slower you'd probably drive off the track. At this point you are committed with your turn in and you will quickly see the first apex as you come over the crest. My preference is to actually put my left tire in the gutter, yes, in the gutter. Your speed if done right will float you nicely and put you in position.

Coming through the first apex you are feeling that the road slopes away from you. Since you are sweeping to the left and the car and having just come over a crest there is a tendency for the car to unweight. This is not good and 99% of all spins in this turn come from those not managing this correctly. To avoid being unweighted and to maintain traction, you must keep some level throttle through the turn. The worst thing you can do at this point is come off the throttle which will cause the car to lighten and the back end will start to wiggle out. Just check out YouTube videos of "mosport spins" and you'll find a few of these.

Holding tight, and if riding on street tires, you will feel tire technology at work as the tire rubber is peeling at the road. The car should naturally track out to about mid track and apex two is in sight. You know you've timed and lined up things nicely as at the bottom of the turn you will feel the car actually sit and hunker down as you tighten up and literally launch towards apex two. At this point traction is plentiful and you apply more throttle to come out of turn two as fast if not faster than when you came in. At apex two you should be full out on throttle and let the car track way out to the right using up all the road.

If you need a little guidance in turn two, luckily two years ago there was a patch of pavement at the bottom of the turn approaching apex two that was re-paved. If you line up with this, you've done well.

A blind corner can be intimidating and even if you miss on the turn-in for apex one, don't worry and don't fight it to try and get back in line, just focus and line up to hit apex two correctly and you'll have plenty of speed coming out. Remember, slow in, fast out.

Wednesday, March 12, 2014

There was a period of time roughly between 2004 and 2010 that I describe as the dark ages for online market research, or more specifically, for anything related to online surveys in general. This was a period of time where the rise of behavioral analytics and a good few years of rapid growth and questionable practices within the industry that put a dark shadow on online surveys.

The market research industry really needed to be held accountable for the mess. Online surveys had become rampant, online panels were growing at an accelerated rate, new panels emerging over night, ridiculous panel acquisitions were taking place, some going public, and the overall practice was questionable on so many fronts. Panels were built by offering people survey points, free iPods, and by downloading spyware through screensaver downloads. The amount of overlap between panels was huge (40%-60% in many cases) and the number of surveys per respondent was increasing.

This ecosystem gave rise to what was known as the professional survey taker. During this period, it seemed that every market research conference was focusing on panel integrity and understanding the professional survey taker. Every talk had the same result. There was inconsistency in results across panels, and within panels.

The core problem with these so-called professional survey takers is that 90%+ of surveys were being taken by less than 1% of the audience. Secondly, due to their behaviors, it was difficult to account for these survey takers to be representative of anyone. The typical survey taker was low income, middle America females, they spent unhealthy amounts of time online, collected coupons, and clicked on everything. Effectively, it was almost impossible to find a nationally representative cross section of people online for research purposes. It was clearly understood and excepted that the online world was not representative of the real world.

Then something changed . . . and what changed was social media arrived.

Once social media arrived, which included Facebook, Twitter and others, the notion of sharing, clicking, providing feedback, comments and other types of user directed contribution, fundamentally changed the profile of the online individual. Facebook quickly grew out of being an early adopter nice group of college students, and quickly there were as many 55+ as there were high school students on the social network. Oprah helped propel twitter into the mainstream and soon it became the exception for someone not to be engaged online.

More than ever people become willing to provide feedback, share their thoughts, post pictures, and more importantly felt they had a voice and that their voice counted. While social media quickly became noisy, online market research surged with the ability to generate consumer feedback in a structured and quantified manner.

It has become very common for media channels to publish survey research results as they quickly found that the data was easy to collect, and readership finds this format easy to digest and very interesting. It is very simple and effective to tweet that 17% of people have sent a tweet while in the bathroom.

Where survey based research was once considered antiquated, not representative and boring, more and more companies began adopting it, or simply showcasing it, as a viable and valuable data source. Of most interest was Google's entry into market research. Yes, the company that stood proud on it's algorithm heavy approach entered the survey market in 2012 with it's Google Consumer Surveys product. In 2012 statistician Nate Silver ranked Google Consumer Surveys second in accuracy over many long time standards such as Gallup, Ipsos, Angus Reid, and CNN during the 2012 Presidential election.

Net result is that the online market research is alive and well and has established itself as a viable data methodology. Thank you social media!

Tuesday, April 16, 2013

My Roku 3 arrived yesterday and was eager to hook it up. Unfortunately I experienced the ever annoying error codes 013 and 014 when trying to connect it to the Internet. Browsing all the forums and the best that Google could do did not solve my problem. As a result I ended up calling their support line and they gave me the instructions to access the 'secret' screens.

The problem is related to outdated software on the Roku which restricts it from connecting whether via wireless or wired.

1. Update Software: On your remote please press the house 5 times, press the fast foward 3 times, then press rewind 2 times. You will get a secret screen, There you have to check your IP address that it is not all 0's. Select the option to update software. The Roku player should now restart.

2. Disable Ping: Once that the roku finish the set up, on the remote please press the Home button 5 times, then the Fast Forward (>>) button 1 time, then the Play button 1 time, then the Rewind (<<) button 1 time, then the Play button 1 time, and then hit the Fast Forward (>>) button 1 time.This will take you to the Platform secret screen. There you have to find the option to disable network ping. Select it and go back to the set up process of the roku player.

About P.Neto

Currently working with a couple cool start-ups as advisor trying to solve some interesting problems. Always looking for an interesting team and problems to conquer.
Previously, Research Director at YuMe.
Co-founder of a tech start-up Crowd Science (acquired by YuMe). Currently trek around the Toronto region but lately spending some serious time in silicon valley and NYC. Spent some good time at SurveySite which later became comScore Networks, and prior to that at CARIS playing with some neat geo-technology.
My blog contains an assortment of thoughts and observations on business, software, advertising, analytics, research, geo-technology and golf. Need to reach me, find me via email at paul (AT) neto (dot) me or on Twitter.